I've gone through this same workflow and had success all the way through DRC ruleset passing with JLCPCB-based settings, ordering, fab, and use.
Board was ordered with PCBA (they did assembly of the stock components) and I did SMD for just the oddball module/ICs they don't stock. Works exactly to design.
My boards are for hobby use and are ridiculously simple though compared to anything professional (breakout boards for specific components in FPV drone video transmission subsystems). That's probably an important detail.
I think its like anything else in AI right now. It can do it 90% of the time but that 10% can be really rough and if its a task you can't do or verify yourself, you won't know the difference.
I became significantly better at electronics soldering by learning to do glasswork (stain glass fixturing, etc). It wasn't intentional and it isn't even the same solder chemistry, but having to do broad asthetics on large pieces meant I "got it" better for the small scale electronics connections.
Good old fashioned machine learning (GOFML?)
Using mixes of different sensor data from industrial 3d printing processes to production quality.
Not as fashionable as LLMs at the moment but there's new innovations to make in the space, so it's honest work.
> Your profile reads like a 'Hacker News Bingo' card: NASA, PhD, Python, 'Ask HN' about cheating, and a strong opinion on Reddit's community. The only thing missing is a post about your custom ergonomic keyboard made from recycled space shuttle parts.
I end up asking the same question when experimenting with tools like Cursor. When it can one-shot a small feature, it works like magic. When it struggles, and the context gets poisoned and I have to roll back commits and retry part of the way through something, it hits a point where it was probably easier for me to just write it. Or maybe template it and have it finish it. Or vice versa. I guess the point being that best practices have yet to truly be established, but totally hands-off uses have not worked well for me so far.
Why commit halfway through implementing something with Cursor? Can you not wait until it’s created a feature or task that has been validated and tests written for it?
Why wait until everything is finalized before committing? Git is distributed/local, so while one philosophy is to interact with it as little as possible, the other one is to commit early and commit often, and easily be able to rollback to a previous (working) state, with the caveat that you clean-up history before firing off a PR.
Well, same statement applies. Rolling back commits is also O(1) and just as easy. And if you branch to start with it's not even a "rollback" through the commit history, it's just a branch switch. Feel like OP has never used git before or something.
Another reason why the idea of AI agents for science hasn't made much sense to me. Research is an extremely collaborative set of activities. How good would a researcher be who is very good at literature review, but never actually talks to anyone, goes to any conferences, etc?
My boards are for hobby use and are ridiculously simple though compared to anything professional (breakout boards for specific components in FPV drone video transmission subsystems). That's probably an important detail. I think its like anything else in AI right now. It can do it 90% of the time but that 10% can be really rough and if its a task you can't do or verify yourself, you won't know the difference.
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